# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

########################################################################
# OPENAPI-URI: /api/mail/mood
########################################################################
# get:
#   responses:
#     '200':
#       content:
#         application/json:
#           schema:
#             $ref: '#/components/schemas/Sloc'
#       description: 200 Response
#     default:
#       content:
#         application/json:
#           schema:
#             $ref: '#/components/schemas/Error'
#       description: unexpected error
#   security:
#   - cookieAuth: []
#   summary: Shows a breakdown of the (analyzed) mood in emails
# post:
#   requestBody:
#     content:
#       application/json:
#         schema:
#           $ref: '#/components/schemas/defaultWidgetArgs'
#   responses:
#     '200':
#       content:
#         application/json:
#           schema:
#             $ref: '#/components/schemas/Sloc'
#       description: 200 Response
#     default:
#       content:
#         application/json:
#           schema:
#             $ref: '#/components/schemas/Error'
#       description: unexpected error
#   security:
#   - cookieAuth: []
#   summary: Shows a breakdown of the (analyzed) mood in emails
#
########################################################################


"""
This is the email mood renderer for Kibble
"""

import json
import time


def run(API, environ, indata, session):

    # We need to be logged in for this!
    if not session.user:
        raise API.exception(403, "You must be logged in to use this API endpoint! %s")

    # First, fetch the view if we have such a thing enabled
    viewList = []
    if indata.get("view"):
        viewList = session.getView(indata.get("view"))
    if indata.get("subfilter"):
        viewList = session.subFilter(indata.get("subfilter"), view=viewList)

    dateTo = indata.get("to", int(time.time()))
    dateFrom = indata.get(
        "from", dateTo - (86400 * 30 * 6)
    )  # Default to a 6 month span

    # Define moods we know of
    moods_good = set(["trust", "joy", "confident", "positive"])
    moods_bad = set(["sadness", "anger", "disgust", "fear", "negative"])
    moods_neutral = set(
        ["anticipation", "surprise", "tentative", "analytical", "neutral"]
    )
    all_moods = set(moods_good | moods_bad | moods_neutral)

    # Start off with a query for the entire org (we want to compare)
    dOrg = session.user["defaultOrganisation"] or "apache"
    query = {
        "query": {
            "bool": {
                "must": [
                    {"range": {"ts": {"from": dateFrom, "to": dateTo}}},
                    {"term": {"organisation": dOrg}},
                    {"exists": {"field": "mood"}},
                ]
            }
        }
    }

    # Count all emails, for averaging scores
    gemls = session.DB.ES.count(index=session.DB.dbname, doc_type="email", body=query)[
        "count"
    ]

    # Add aggregations for moods
    query["aggs"] = {}
    for mood in all_moods:
        query["aggs"][mood] = {"sum": {"field": "mood.%s" % mood}}

    global_mood_compiled = {}
    mood_compiled = {}
    txt = "This chart shows the ten potential mood types as they average on the emails in this period. A score of 100 means a sentiment is highly visible in most emails."
    gtxt = "This shows the overall estimated mood as a gauge from terrible to good."
    # If we're comparing against all lists, first do a global query
    # and compile moods overall
    if indata.get("relative"):
        txt = "This chart shows the ten potential mood types on the selected lists as they compare against all mailing lists in the database. A score of 100 here means the sentiment conforms to averages across all lists."
        gtxt = "This shows the overall estimated mood compared to all lists, as a gauge from terrible to good."
        global_moods = {}

        gres = session.DB.ES.search(
            index=session.DB.dbname, doc_type="email", size=0, body=query
        )
        for mood, el in gres["aggregations"].items():
            # If a mood is not present (iow sum is 0), remove it from the equation by setting to -1
            if el["value"] == 0:
                el["value"] == -1
            global_moods[mood] = el["value"]
        for k, v in global_moods.items():
            if v >= 0:
                global_mood_compiled[k] = int((v / max(1, gemls)) * 100)

    # Now, if we have a view (or not distinguishing), ...
    ss = False
    if indata.get("source"):
        query["query"]["bool"]["must"].append(
            {"term": {"sourceID": indata.get("source")}}
        )
        ss = True
    elif viewList:
        query["query"]["bool"]["must"].append({"terms": {"sourceID": viewList}})
        ss = True

    # If we have a view enabled (and distinguish), compile local view against global view
    # Else, just copy global as local
    if ss or not indata.get("relative"):
        res = session.DB.ES.search(
            index=session.DB.dbname, doc_type="email", size=0, body=query
        )

        del query["aggs"]  # we have to remove these to do a count()
        emls = session.DB.ES.count(
            index=session.DB.dbname, doc_type="email", body=query
        )["count"]

        moods = {}
        years = 0

        for mood, el in res["aggregations"].items():
            if el["value"] == 0:
                el["value"] == -1
            moods[mood] = el["value"]
        for k, v in moods.items():
            if v > 0:
                mood_compiled[k] = int(
                    100
                    * int((v / max(1, emls)) * 100)
                    / max(1, global_mood_compiled.get(k, 100))
                )
    else:
        mood_compiled = global_mood_compiled

    # If relative mode and a field is missing, assume 100 (norm)
    if indata.get("relative"):
        for M in all_moods:
            if mood_compiled.get(M, 0) == 0:
                mood_compiled[M] = 100

    # Compile an overall happiness level
    MAX = max(max(mood_compiled.values()), 1)
    X = 100 if indata.get("relative") else 0
    bads = X
    for B in moods_bad:
        if mood_compiled.get(B) and mood_compiled[B] > X:
            bads += mood_compiled[B]

    happ = 50

    goods = X
    for B in moods_good:
        if mood_compiled.get(B) and mood_compiled[B] > X:
            goods += mood_compiled[B]
    MAX = max(MAX, bads, goods)
    if bads > 0:
        happ -= 50 * bads / MAX
    if goods > 0:
        happ += 50 * goods / MAX
    swingometer = max(0, min(100, happ))

    # JSON out!
    JSON_OUT = {
        "relativeMode": True,
        "text": txt,
        "counts": mood_compiled,
        "okay": True,
        "gauge": {"key": "Happiness", "value": swingometer, "text": gtxt},
    }
    yield json.dumps(JSON_OUT)
